Evidence map›Paper›PMID 30580259›Full record

Observational studyBMJ open2018

Patient work from a context and time use perspective: a mixed-methods study protocol.

Kathleen Yin, Teresa Harms, Kenneth Ho, Frances Rapport, Sanjyot Vagholkar, Liliana Laranjo, Enrico Coiera, Jonathan Gershuny, Annie Y S Lau

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in BMJ open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.2field-weighted citation impact, top 41% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 7 citations in OpenAlex.

  1. The work of managing a chronic illness: A job description.Journal of evaluation in clinical practice · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 3 countries.

Kathleen YinCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.ORCID 0000-0001-8004-9805
Teresa HarmsDepartment of Sociology, Centre for Time Use Research, University of Oxford, Oxford, UK.
Kenneth HoFaculty of Medical and Health Sciences, Macquarie University, Sydney, New South Wales, Australia.
Frances RapportCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.
Sanjyot VagholkarFaculty of Medical and Health Sciences, Macquarie University, Sydney, New South Wales, Australia.
Liliana LaranjoCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.
Enrico CoieraCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.ORCID 0000-0002-6444-6584
Jonathan GershunyDepartment of Sociology, Centre for Time Use Research, University of Oxford, Oxford, UK.
Annie Y S LauCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.
Macquarie University · AUThe University of Western Australia · AUUniversity of Oxford · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionSelf-management is widely promoted but less attention is focused on the work required from patients. To date, many individuals struggle to practise self-management. 'Patient work', a concept that examines the 'work' involved in self-management, is an approach to understanding the tasks, effort, time and context from patient perspective. The purpose of our study is to use a novel approach combining non-obstructive observations via digital devices with in-depth qualitative data about health behaviours and motivations, to capture the full range of patient work experienced by people with type 2 diabetes and chronic comorbidities. It aims to yield comprehensive insights about 'what works' in self-management, potentially extending to populations with other chronic health conditions. METHODS AND ANALYSIS: This mixed-methods observational study involves a (1) prestudy interview and questionnaires, (2) a 24-hour period during which participants wear a camera and complete a time-use diary, and a (3) poststudy interview and study feedback. Adult participants living with type 2 diabetes with at least one chronic comorbidity will be recruited using purposive sampling to obtain a balanced gender ratio and of participants using insulin and those using only oral medication. Interviews will be analysed using thematic analysis. Data captured by digital devices, diaries and questionnaires will be used to analyse the duration, time, context and patterns of health-related behaviours. ETHICS AND DISSEMINATION: The study was approved by the Macquarie University Human Research Ethics Committee for Medical Sciences (reference number 5201700718). Participants will carry a wallet-sized card that explains the purpose of the study to third parties, and can remove the camera at any stage. Before the poststudy interview begins, participants will view the camera images in private and can delete any images. Should any images be used in future publications or presentations, identifying features such as human faces and names will be obscured.

Indexed as

ComorbidityEmploymentTask Performance and AnalysisAdultChronic DiseaseDatabases, FactualDiabetes Mellitus, Type 2FemaleHealth BehaviorHumansMaleMonitoring, PhysiologicPatient CompliancePatient SelectionQualitative ResearchSelf-Managementbody-worn camerasburden of diseasemixed-methods studypassive data collectionpatient worktime-use diary

Identifiers

PMID30580259
PMCPMC6307620
OpenAlexW2906544550

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.